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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) SATIN - Sains dan Teknologi Informasi
Sari, Tri Kartika
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Automotive Engineering Computer Science & IT Control & Systems Engineering

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Study Orange Data Mining Model Prediksi Status Gizi Balita Kelurahan X Sari, Tri Kartika; Imam Yuadi
SATIN - Sains dan Teknologi Informasi Vol 9 No 2 (2023): SATIN - Sains dan Teknologi Informasi
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/stn.v9i2.994

Abstract

Usia 0-59 bulan merupakan periode emas masa penting dimana semua proses perkembangan organ yang mempengaruhi kemampuan sensorik dan motorik seorang anak berlangsung. Pemantauan dan identifikasi status gizi balita secara dini diharapkan bisa melakukan control serta intervensi yang tepat dan cepat sehingga bisa menghilangkan atau meminimalisir dampak buruk yang ditimbulkan. Tujuan penelitian ini untuk melakukan identifikasi status gizi balita melalui pembuatan model prediksi menggunakan aplikasi orange data mining dan memberikan rekomendasi metode algoritma mana diantara KNN, Decision Tree, Naive Bayesdan Regresi logistic yang paling akurat.ROC Analisys (ROCA),Cross Validation dan Confusion Matrixsebagai model evaluasi. Empat model tersebutkemudian dibandingkan dan disimpulkan bahwa model algoritma KNN yang lebih direkomendasikan untuk prediksi status gizi karena memiliki tingkat akurasi dan presisi lebih baik dibanding 3 metode lainnya dengan nilai akurasi 95,24%, presisi 77,51%.
Classification Analysis of Student Ability in Learning Using Clustering Method at SMA Tunas Pelita Nurhayati; Sitompul, Juliana Naftali; Sari, Tri Kartika
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 1 No. 1 (2021): October 2021
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (410.118 KB) | DOI: 10.59934/jaiea.v1i1.46

Abstract

This study aims to classify the assessment of the learning process at SMA Tunas Pelita Binjai T.A. 2018/2019 based on the average grade X, additional subjects applied technology, and student absenteeism classified using Matlab.The data is processed based on learning grouping as much as 2 clusters with different centroids, namely for cluster 1 the average value of even and odd semesters for class X (85.0), additional subjects of applied technology (86.3) and student attendance (2.4) and cluster 2 the average grades of odd-even semesters for class X (68.2), additional subjects of applied technology (70.3) and student attendance (2.4). In the final result, it can be seen that the grouping of learning at SMA Tunas Pelita Binjai with 100 data can be divided into 2 groups, namely group 1 with 62 data with an average value of odd and even semesters and high additional applied technology and student absenteeism. low grades are classified as students with good grades and group 2 as many as 38 data with an average value of odd, even semesters and low values ​​of applied technology and high student absenteeism belonging to students who have poor grades.
Family Economic Correlation To Students Learning Achievment Using Apriori Method Nurhayati; Sitompul, Juliana Naftali; Sari, Tri Kartika
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 1 No. 1 (2021): October 2021
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (427.375 KB) | DOI: 10.59934/jaiea.v1i1.55

Abstract

The education system in Indonesia as mandated in the GBHN aims to educate the nation while at the same time responding to new challenges to create a decent and prosperous life. Understanding, apprecation, and experience of cultural and religious values in the right and true form will be increasingly needed. The economic status of the family is one of the factors that is sufficient to support the level of continuing education, especially for teenagers who are still student in school. Apriori method is used to obtain association rules that describe the relationship between item in the transactional database. There are two databases used, each of which has a different number of transactions. This study aims to aplly the apriori algorithm, as an analytical technique. The data taken as a case example is familiy economic data. This association search uses WEKA which will later find the rules and MySQL as the placeholder for the Database. From the results of the analysis using apriori, the highest confidence value was obtained at 0.9 with support 0.1 resulting in a students rule whose economics supported the learning achievement was very supportive, and the lowest confidence value of 0.2 with support 0.1 resulted in a students rule who had sufficient economics, so their learning achievement was also quite increased..
Study Orange Data Mining Model Prediksi Status Gizi Balita Kelurahan X Sari, Tri Kartika; Imam Yuadi
SATIN - Sains dan Teknologi Informasi Vol 9 No 2 (2023): SATIN - Sains dan Teknologi Informasi
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/stn.v9i2.994

Abstract

Usia 0-59 bulan merupakan periode emas masa penting dimana semua proses perkembangan organ yang mempengaruhi kemampuan sensorik dan motorik seorang anak berlangsung. Pemantauan dan identifikasi status gizi balita secara dini diharapkan bisa melakukan control serta intervensi yang tepat dan cepat sehingga bisa menghilangkan atau meminimalisir dampak buruk yang ditimbulkan. Tujuan penelitian ini untuk melakukan identifikasi status gizi balita melalui pembuatan model prediksi menggunakan aplikasi orange data mining dan memberikan rekomendasi metode algoritma mana diantara KNN, Decision Tree, Naive Bayesdan Regresi logistic yang paling akurat.ROC Analisys (ROCA),Cross Validation dan Confusion Matrixsebagai model evaluasi. Empat model tersebutkemudian dibandingkan dan disimpulkan bahwa model algoritma KNN yang lebih direkomendasikan untuk prediksi status gizi karena memiliki tingkat akurasi dan presisi lebih baik dibanding 3 metode lainnya dengan nilai akurasi 95,24%, presisi 77,51%.
Co-Authors Imam Yuadi Nurhayati